{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sea surface temperature\n",
    "\n",
    "This is an example of how to convert publicly available raster data to dfs2.\n",
    "\n",
    "Data are provided in gzipped netcdf.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gzip\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import xarray\n",
    "import urllib.request"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('20150101-DMI-L4UHfnd-NSEABALTIC-v01-fv01-DMI_OI.nc.gz',\n",
       " <http.client.HTTPMessage at 0x1b6613f7f08>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://data.nodc.noaa.gov/ghrsst/L4/NSEABALTIC/DMI/DMI_OI/2015/001/20150101-DMI-L4UHfnd-NSEABALTIC-v01-fv01-DMI_OI.nc.gz\"\n",
    "filename = \"20150101-DMI-L4UHfnd-NSEABALTIC-v01-fv01-DMI_OI.nc\"\n",
    "gzfilename = filename + \".gz\"\n",
    "\n",
    "urllib.request.urlretrieve (url, gzfilename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unzip file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with gzip.open(gzfilename, 'rb') as f_in:\n",
    "    with open(filename, 'wb') as f_out:\n",
    "        shutil.copyfileobj(f_in, f_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (lat: 600, lon: 1334, time: 1)\n",
       "Coordinates:\n",
       "  * lon             (lon) float32 -9.99 -9.96 -9.93 ... 29.939999 29.969997 30.0\n",
       "  * lat             (lat) float32 48.01 48.04 48.07 ... 65.92 65.95 65.979996\n",
       "  * time            (time) object 2015-01-01 00:00:00\n",
       "Data variables:\n",
       "    analysed_sst    (time, lat, lon) float32 ...\n",
       "    analysis_error  (time, lat, lon) float32 ...\n",
       "    mask            (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:            CF-1.0\n",
       "    title:                  DMI Sea Surface Temperature analysis\n",
       "    GDS_version_id:         v1.0-rev1.7\n",
       "    netcdf_version_id:      netcdf-3.6.1-beta3 of Jan  9 2006 03:17:05\n",
       "    DSD_entry_id:           DMI-L4UHfnd-NSEABALTIC-DMI_OI\n",
       "    institution:            Center for Ocean and Ice, Danish Meteorological I...\n",
       "    references:             http://ocean.dmi.dk\n",
       "    contact:                jlh@dmi.dk\n",
       "    creation_date:          2015-01-01T07:24:31 UTC\n",
       "    product_version:        Version 1.0\n",
       "    history:                Version 1.0\n",
       "    spatial_resolution:       0.03 degrees\n",
       "    start_date:             2015-01-01\n",
       "    start_time:             00:00:00\n",
       "    stop_date:              2015-01-02\n",
       "    stop_time:              00:00:00\n",
       "    source_data:            Nighttime AMSRE,ATS_NR_2P,AVHRR18_G,AVHRR17_NAR,A...\n",
       "    westernmost_longitude:  -9.99\n",
       "    easternmost_longitude:  30.0\n",
       "    southernmost_latitude:  48.01\n",
       "    northernmost_latitude:  65.979996\n",
       "    file_quality_index:     0\n",
       "    comment:                IN NO EVENT SHALL DMI OR ITS REPRESENTATIVES BE L...</pre>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (lat: 600, lon: 1334, time: 1)\n",
       "Coordinates:\n",
       "  * lon             (lon) float32 -9.99 -9.96 -9.93 ... 29.939999 29.969997 30.0\n",
       "  * lat             (lat) float32 48.01 48.04 48.07 ... 65.92 65.95 65.979996\n",
       "  * time            (time) object 2015-01-01 00:00:00\n",
       "Data variables:\n",
       "    analysed_sst    (time, lat, lon) float32 ...\n",
       "    analysis_error  (time, lat, lon) float32 ...\n",
       "    mask            (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:            CF-1.0\n",
       "    title:                  DMI Sea Surface Temperature analysis\n",
       "    GDS_version_id:         v1.0-rev1.7\n",
       "    netcdf_version_id:      netcdf-3.6.1-beta3 of Jan  9 2006 03:17:05\n",
       "    DSD_entry_id:           DMI-L4UHfnd-NSEABALTIC-DMI_OI\n",
       "    institution:            Center for Ocean and Ice, Danish Meteorological I...\n",
       "    references:             http://ocean.dmi.dk\n",
       "    contact:                jlh@dmi.dk\n",
       "    creation_date:          2015-01-01T07:24:31 UTC\n",
       "    product_version:        Version 1.0\n",
       "    history:                Version 1.0\n",
       "    spatial_resolution:       0.03 degrees\n",
       "    start_date:             2015-01-01\n",
       "    start_time:             00:00:00\n",
       "    stop_date:              2015-01-02\n",
       "    stop_time:              00:00:00\n",
       "    source_data:            Nighttime AMSRE,ATS_NR_2P,AVHRR18_G,AVHRR17_NAR,A...\n",
       "    westernmost_longitude:  -9.99\n",
       "    easternmost_longitude:  30.0\n",
       "    southernmost_latitude:  48.01\n",
       "    northernmost_latitude:  65.979996\n",
       "    file_quality_index:     0\n",
       "    comment:                IN NO EVENT SHALL DMI OR ITS REPRESENTATIVES BE L..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xarray.open_dataset(filename)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1b662531b08>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.analysed_sst[0].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 600, 1334)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst = ds.analysed_sst[:,:,:].values\n",
    "sst.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The input dataset dimensions are ordered (time, lon, lat).\n",
    "\n",
    "This is the expected order\n",
    "\n",
    "But the image have to be flipped upside down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "sst[0] = np.flipud(sst[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-9.99, 48.01, 1334, 600, 0.029977509643005645, 0.029949995676676434)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat = ds.lat.values\n",
    "lon = ds.lon.values\n",
    "\n",
    "\n",
    "nx = len(lon)\n",
    "ny = len(lat)\n",
    "\n",
    "x0 = lon[0]\n",
    "y0 = lat[0]\n",
    "\n",
    "\n",
    "dx = (lon[-1] - lon[0]) / nx\n",
    "dy = (lat[-1] - lat[0]) / ny\n",
    "\n",
    "x0, y0, nx, ny, dx, dy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015-01-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(ds.time.values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "start_time = datetime(2015,1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variable types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mikeio.dfs_util import type_list, unit_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{100006: 'Temperature',\n",
       " 100189: 'Temperature lapse rate',\n",
       " 100191: 'Temperature correction',\n",
       " 100200: 'Temperature Flux'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type_list(\"temperature\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2800: 'degree Celsius', 2801: 'degree Fahrenheit', 2802: 'degree Kelvin'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variable_type = 100006\n",
    "\n",
    "unit_list(variable_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "unit = 2802"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create dfs2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mikeio.dfs2 import Dfs2\n",
    "\n",
    "d = []\n",
    "d.append(sst)\n",
    "\n",
    "dfsfilename = filename.replace(\".nc\", \".dfs2\")\n",
    "\n",
    "coordinate = ['LONG/LAT', x0, y0, 0]\n",
    "\n",
    "dfs = Dfs2()\n",
    "dfs.create(filename=dfsfilename,\n",
    "           data=d,\n",
    "           start_time = start_time, \n",
    "           coordinate=coordinate, length_x=dx, length_y=dy,\n",
    "           names=[\"Sea surface temperature\"], \n",
    "           variable_type=[variable_type], unit=[unit]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dfs2\n",
    "\n",
    "![dfs2](../images/sst_dfs2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "ds.close()\n",
    "os.remove(gzfilename)\n",
    "os.remove(filename)\n",
    "os.remove(dfsfilename)"
   ]
  }
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